R Notebook

Loading in the data

data_all <-  read_csv("../../../reports/runtimes_big.csv", col_types = cols(num_students = col_factor(), num_projects = col_factor(), proj_pref_type = col_factor(), mechanism = col_factor()))

data_singleton <- data_all %>% filter(proj_pref_type == "singleton")
data_p1 <- data_all %>% filter(proj_pref_type == "linear_perturbed_1")
data_p4 <- data_all %>% filter(proj_pref_type == "linear_perturbed_4")
data_r <- data_all %>% filter(proj_pref_type == "random")


colors_mechanism = c("#7a5195", "#ffa600")

Some initial playing with scatterplot3d

library(scatterplot3d)

plotit <- function(data) {
    
    #colors <- c("#003f5c", "#58508d", "#bc5090", "#ff6361", "#ffa600")
    colors = c("#003f5c" ,"#7a5195" ,"#ef5675" ,"#ffa600")
    colors_mechanism = c("#7a5195", "#ffa600")
    
    shapes <- c(15, 16, 17, 18)
    
    scatterplot3d(
        x = data$num_projects,
        y = data$num_students,
        z = data$duration_ms / 1000, 
        pch = shapes[factor(data$proj_pref_type)],
        color = colors_mechanism[factor(data$mechanism)]
        #,zlim = c(0,15)
    )
    
}

plotit(filter(data_all, num_slots == 1))

plotit(data_singleton)

plotit(data_p1)

plotit(data_p4)

plotit(data_r)

These are some other plots

Using plotly

library(plotly)

Warning: package ‘plotly’ was built under R version 4.0.5

Attaching package: ‘plotly’

The following object is masked from ‘package:ggplot2’:

    last_plot

The following object is masked from ‘package:stats’:

    filter

The following object is masked from ‘package:graphics’:

    layout

plot_data <- function(data) {
    fig <- plot_ly(data, x = ~num_projects, y = ~num_students, z = ~(duration_ms / 1000), color = ~as.factor(mechanism), colors = colors_mechanism) %>%
        add_markers(size=1) %>% 
        layout(
            scene = list(
                xaxis = list(title = "# projects"),
                yaxis = list(title = "# students"),
                zaxis = list(title = "duration (sec)", type = "log")
            ),
            legend = list(orientation = "h")
        )
    
    return(fig)
}

plot_data(data_all)

plot_data(data_singleton)

plot_data(data_p1)

plot_data(data_p4)

plot_data(data_r)